Rigor

Rigor in data management means ensuring that the practices surrounding data collection, processing, and analysis uphold the standards of sound scientific methodology. Rigorous data management helps prevent:

Accidental data corruption or loss.
Silent errors in processing pipelines.
Confusion between different versions or stages of data.
Unintentional mixing of training and test data, or other methodological mistakes.

STAMPED principles promote rigor by enforcing structure and discipline: version control prevents silent overwrites, provenance tracking creates an auditable trail, automated pipelines reduce human error, and clear separation of raw and processed data guards against contamination. When the data management infrastructure itself enforces good practices, researchers are less likely to make mistakes that compromise their results.

Walkthrough: stellar distances from Gaia parallax

12 March 2026·14 mins

Reproducibility Rigor Transparency Reusable Accessible Workflow S — Self-contained T — Tracked A — Actionable M — Modular P — Portable E — Ephemeral D — Distributable STAMPED-intro gaia parallax walkthrough python datalad make

Incrementally builds a research object from a bare script to a tracked, portable, reproducible pipeline — motivated by real problems, not by acronym order.

From Script to STAMPED Research Object

20 February 2026·9 mins

Reproducibility Rigor Transparency Reusable Accessible Pattern S — Self-contained T — Tracked A — Actionable M — Modular P — Portable E — Ephemeral D — Distributable STAMPED-intro shell POSIX awk make git singularity containers

Four scenarios showing how to incrementally add STAMPED properties to a shell-based analysis using git, make, and singularity.

Ephemeral Shell Scripts for Reproducing Issues

19 February 2026·6 mins

Reproducibility Rigor Transparency Reusable Accessible Pattern S — Self-contained A — Actionable E — Ephemeral P — Portable shell POSIX reproducer bug-report testing

Distills a common practice among open-source developers: writing throwaway shell scripts that set up a fresh environment, reproduce a problem, and can be shared as actionable bug reports or starting points for test cases.

Git Submodules for Modular Dataset Composition

19 February 2026·6 mins

Reproducibility Rigor Interoperable Reusable Tool M — Modular T — Tracked git submodules dataset-organization

Demonstrates how git submodules enable independent versioning and composition of dataset components.

Re-executing Computations with 'datalad rerun'

19 February 2026·7 mins

Reproducibility Rigor Reusable Tool A — Actionable E — Ephemeral datalad reproducibility re-execution

Demonstrates how datalad rerun enables re-execution of previously recorded ‘datalad run’ commands, turning provenance records into actionable recipes.